<p>The identification of fish species is crucial for aquaculture, quality control, and biodiversity conservation. However, despite the growing volume of research in pattern recognition, deep learning, and image processing, there are still single-model limitations due to morphological variability and imbalanced datasets that lead to overfitting and poor generalization. We propose a new paradigm of ensemble learning for fish classification by exploring the complementary capabilities of three structurally diverse convolutional neural networks. Our method combines the outputs of the different models and achieves 99.67% accuracy on benchmark fish datasets, leading to increased robustness and generalizability across body, head, and scale features. Extensive evaluation confirms that multi-model fusion improves classification, demonstrating the power of ensemble learning for robust fish identification, with very promising applications in research and industry.</p>